A flexible sequence alignment approach on pattern mining and matching for human activity recognition

Po Cheng Huang, Sz Shian Lee, Yaw Huang Kuo, Kuan Rong Lee

研究成果: Article同行評審

24 引文 斯高帕斯(Scopus)

摘要

This paper proposes a flexible sequence alignment approach for pattern mining and matching in the recognition of human activities. During pattern mining, the proposed sequence alignment algorithm is invoked to extract out the representative patterns which denote specific activities of a person from the training patterns. It features high performance and robustness on pattern diversity. Besides, the algorithm evaluates the appearance probability of each pattern as weight and allows adapting pattern length to various human activities. Both of them are able to improve the accuracy of activity recognition. In pattern matching, the proposed algorithm adopts a dynamic programming based strategy to evaluate the correlation degree between each representative activity pattern and the observed activity sequence. It can avoid the trouble on segmenting the observed sequence. Moreover, we are able to obtain recognition results continuously. Besides, the proposed matching algorithm favors recognition of concurrent human activities with parallel matching. The experimental result confirms the high accuracy of human activity recognition by the proposed approach.

原文English
頁(從 - 到)298-306
頁數9
期刊Expert Systems With Applications
37
發行號1
DOIs
出版狀態Published - 2010 一月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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